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Recommending Search Filters To Improve Conversions At Airbnb

Hao Li, Kedar Bellare, Siyu Yang, Sherry Chen, Liwei He, Stephanie Moyerman, Sanjeev Katariya

TL;DR

This work introduces a modeling framework that directly targets lower-funnel conversions (bookings) by recommending intermediate tools, i.e. search filters, and ensures that search filters serve their ultimate purpose at Airbnb - helping guests find and book their ideal accommodations.

Abstract

Airbnb, a two-sided online marketplace connecting guests and hosts, offers a diverse and unique inventory of accommodations, experiences, and services. Search filters play an important role in helping guests navigate this variety by refining search results to align with their needs. Yet, while search filters are designed to facilitate conversions in online marketplaces, their direct impact on driving conversions remains underexplored in the existing literature. This paper bridges this gap by presenting a novel application of machine learning techniques to recommend search filters aimed at improving booking conversions. We introduce a modeling framework that directly targets lower-funnel conversions (bookings) by recommending intermediate tools, i.e. search filters. Leveraging the framework, we designed and built the filter recommendation system at Airbnb from the ground up, addressing challenges like cold start and stringent serving requirements. The filter recommendation system we developed has been successfully deployed at Airbnb, powering multiple user interfaces and driving incremental booking conversion lifts, as validated through online A/B testing. An ablation study further validates the effectiveness of our approach and key design choices. By focusing on conversion-oriented filter recommendations, our work ensures that search filters serve their ultimate purpose at Airbnb - helping guests find and book their ideal accommodations.

Recommending Search Filters To Improve Conversions At Airbnb

TL;DR

This work introduces a modeling framework that directly targets lower-funnel conversions (bookings) by recommending intermediate tools, i.e. search filters, and ensures that search filters serve their ultimate purpose at Airbnb - helping guests find and book their ideal accommodations.

Abstract

Airbnb, a two-sided online marketplace connecting guests and hosts, offers a diverse and unique inventory of accommodations, experiences, and services. Search filters play an important role in helping guests navigate this variety by refining search results to align with their needs. Yet, while search filters are designed to facilitate conversions in online marketplaces, their direct impact on driving conversions remains underexplored in the existing literature. This paper bridges this gap by presenting a novel application of machine learning techniques to recommend search filters aimed at improving booking conversions. We introduce a modeling framework that directly targets lower-funnel conversions (bookings) by recommending intermediate tools, i.e. search filters. Leveraging the framework, we designed and built the filter recommendation system at Airbnb from the ground up, addressing challenges like cold start and stringent serving requirements. The filter recommendation system we developed has been successfully deployed at Airbnb, powering multiple user interfaces and driving incremental booking conversion lifts, as validated through online A/B testing. An ablation study further validates the effectiveness of our approach and key design choices. By focusing on conversion-oriented filter recommendations, our work ensures that search filters serve their ultimate purpose at Airbnb - helping guests find and book their ideal accommodations.
Paper Structure (24 sections, 7 equations, 10 figures, 7 tables)

This paper contains 24 sections, 7 equations, 10 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Motivating Example
  4. Modeling Framework
  5. Problem Formulation
  6. Training Data
  7. Training Example
  8. Training Labels
  9. Model Features
  10. Model Architecture
  11. Productionization and Practical Considerations
  12. Serving Architecture
  13. Latency and QPS optimization
  14. Enabling item-aware filter recommendation
  15. Cold Start
  16. ...and 9 more sections

Figures (10)

  • Figure 1: Recommending filters in the “Recommended for you” section in the filter panel
  • Figure 2: Recommending amenity filters in the amenities section in the filter panel
  • Figure 3: Recommending filters in the filter bar
  • Figure 4: Decision framework for filter recommendation strategies based on conversion rate variability
  • Figure 5: Illustration of filter usage and booking attribution within an example Airbnb user search journey. The search journey is analyzed retrospectively to attribute the booking to preceding searches. Since listing e was booked, Search #2 is labeled negative (B=0) as the filter applied excluded the booked listing. Searches #1, #3 and #4 are labeled positive (B=1) as their respective filters applied all included the booked listing.
  • ...and 5 more figures